Estimation of Contamination of ERS-2 and POSEIDON Satellite Radar Altimetry Close to the Coasts of Australia

It is broadly acknowledged that the precision of satellite-altimeter-measured instantaneous sea surface heights (SSH) is lower in coastal regions than in open oceans, due partly to contamination of the radar return from the coastal sea-surface state and from land topography. This study investigates the behavior of ERS-2 and POSEIDON altimeter waveform data in coastal regions and estimates a boundary around Australia's coasts in which the altimeter range may be poorly estimated by on-satellite tracking software. Over one million 20 Hz ERS-2 (March to April 1999) and POSEIDON (January 1998 to January 1999) radar altimeter waveform data were used over an area extending 350 km offshore Australia. The DS759.2 (5'resolution) ocean depth model and the GSHHS (0.2 km resolution) shoreline model were used together to define the coastal regions. Using the 50% threshold retracking points as the estimates of expected tracking gate, we determined that the sea surface height is contaminated out to maximum distance of between about 8 km and 22 km from the Australian shoreline for ERS-2, depending partly on coastal topography. Using the standard deviation of the mean waveforms as an indication of the general variability of the altimeter returns in the Australian coastal region shows obvious coastal contamination out to about 4 km for both altimeters, and less obvious contamination out to about 8 km for POSEIDON and 10 km for ERS-2. Therefore, ERS-2 and POSEIDON satellite altimeter data should be treated with some caution for distances less than about 22 km from the Australian coast and probably ignored altogether for distances less than 4 km.     

In situ determination on food consumption and ecological conversion efficiency of a marine fish species, Hyporhamphus sajori

The food consumption and ecological conversion efficiency of a species marine pelagic andsmall size fish, Hyporhamphus sajori, were determined by using in situ stomach content method presented by Eggers. The results showed that: (1) the fish was taken in food all day, so empty-stomach rate was very low, taking up about 4.5% of the total determined fish number. However, the fish still has significant daily feeding rhythm. A feeding peak was found 0:00 o'clock at night, but feeding level was always high in the daytime; (2) relationship between instantaneous food content in stomach and corresponding time could be described as S_t = a ·e ~(-b ·t). There was not significant difference of instantaneous gastric evacuation rate between two determinations, with average value being 0.13 × 10~(-2)g/ (g·d) (wet weight); (3) the daily food consumption tended to change in irregular waving form, with average value being (10.16×l0~(-2)±1.19×l0~(-2) g/ (g·d) (wet weight) or (55.56 × 10~(-2) ±6.51 × 10~(-2) kJ/     

The impact of the M2 internal tide on data-assimilative model estimates of the surface tide

We use a synthetic data experiment to assess the accuracy of ocean tides estimated from satellite altimetry data, with emphasis on the impact of the phase-locked internal tide, which has a surface expression of several centimeters near its sites of genesis. Previous tidal estimates have regarded this signal as a random measurement error; however, it is deterministic and not scale-separated from the barotropic (surface) tide around complex bathymetric features. The synthetic data experiments show that the internal tide has a negligible impact on the barotropic tidal fields inferred under these circumstances, and the barotropic dissipation (a quadratic functional of the tidal fields) is in good agreement with the energetics of the three-dimensional regional primitive equations model which is the source of the synthetic data.     

Neural network for wave forecasting among multi-stations

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data.     

Data assimilation for a coastal area morphodynamic model: Morecambe Bay

This paper investigates the use of data assimilation in coastal area morphodynamic modelling using Morecambe Bay as a study site. A simple model of the bay has been enhanced with a data assimilation scheme to better predict large-scale changes in bathymetry observed in the bay over a 3-year period. The 2DH decoupled morphodynamic model developed for the work is described, as is the optimal interpolation scheme used to assimilate waterline observations into the model run. Each waterline was acquired from a SAR satellite image and is essentially a contour of the bathymetry at some level within the inter-tidal zone of the bay. For model parameters calibrated against validation observations, model performance is good, even without data assimilation. However the use of data assimilation successfully compensates for a particular failing of the model, and helps to keep the model bathymetry on track. It also improves the ability of the model to predict future bathymetry. Although the benefits of data assimilation are demonstrated using waterline observations, any observations of morphology could potentially be used. These results suggest that data assimilation should be considered for use in future coastal area morphodynamic models.     

A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes

It is important to estimate hard-to-observe parameters in the ocean interior from easy-to-observe parameters. This study therefore demostrates a reconstruction of observed temperature and salinity profiles of the sea east of Japan (30°≈40°N, 140°≈150°E). The reconstruction was done by estimating suboptimal state from several values of the observed profiles and/or sea surface dynamic height (SDH) calculated from the profiles. The estimation used a variational method with vertical coupled temperature-salinity empirical orthogonal function (EOF) modes. Profiles of temperature and salinity in the subtropical region are effectively reconstructed from in situ temperature profile data, or sea surface temperature (SST) and SDH. For example, the analyzed temperature field from SST and SDH has an accuracy to within 1°C in the subtropical region. Salinity in the sea north of Kuroshio, however, is difficult to estimate because of its complex variability which is less correlated with temperature than in the subtropical region. Sea surface salinity is useful to estimate the subsurface structure. We also show the possibility that the estimation is improved by considering nonlinearity in the equation calculating SDH from temperature and salinity analysis values in order to examine the misfit between analysis and observation. Analysis using TOPEX/POSEIDON altimetry data instead of SDH was also performed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Seakeeping assessment of fishing vessels in conceptual design stage

The main idea of this paper is to identify functional relations between seakeeping characteristics and hull form parameters of Mediterranean fishing vessels. Multiple regression analysis is used for quantitative assessment through a computer software that is based on the SQL Server Database. The seakeeping attributes under investigation are the transfer functions of heave and pitch motions and of absolute vertical acceleration at stern, while the ship parameters influencing motion dynamics have been classified into two groups: displacement (Δ) and main dimensions (L, B, T), coefficients that define the details of the hull form (C_WP, C_VP, LCB, LCF, etc.).Four multiple regression models having different parameter combinations are here investigated and discussed, giving way to the so-called ‘Simple Model’, ‘Intermediate Model’, ‘Enhanced 1 Model’ and ‘Enhanced 2 Model’. The obtained results are more than satisfactory for seakeeping predictions during the conceptual design stage.     

Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar)

I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect.     

山东半岛东部,北部地区冬季冷流低云的探讨

本文主要对山东半岛东部、北部及渤海海峡地区的地方性天气－冷流低云，作了初步的分析。着重分析产生这种低云的环流特点及殊地理环境，分析归纳出这种低云的预测方法及判据。     

Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation

A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.